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</style><section id="model-selection-and-evaluation">
<span id="model-selection"></span><h1><span class="section-number">3. </span>Model selection and evaluation<a class="headerlink" href="#model-selection-and-evaluation" title="Link to this heading">¶</a></h1>
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<ul>
<li class="toctree-l1"><a class="reference internal" href="modules/cross_validation.html">3.1. Cross-validation: evaluating estimator performance</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_validation.html#computing-cross-validated-metrics">3.1.1. Computing cross-validated metrics</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_validation.html#cross-validation-iterators">3.1.2. Cross validation iterators</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_validation.html#a-note-on-shuffling">3.1.3. A note on shuffling</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_validation.html#cross-validation-and-model-selection">3.1.4. Cross validation and model selection</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/cross_validation.html#permutation-test-score">3.1.5. Permutation test score</a></li>
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<li class="toctree-l1"><a class="reference internal" href="modules/grid_search.html">3.2. Tuning the hyper-parameters of an estimator</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/grid_search.html#exhaustive-grid-search">3.2.1. Exhaustive Grid Search</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/grid_search.html#randomized-parameter-optimization">3.2.2. Randomized Parameter Optimization</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/grid_search.html#searching-for-optimal-parameters-with-successive-halving">3.2.3. Searching for optimal parameters with successive halving</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/grid_search.html#tips-for-parameter-search">3.2.4. Tips for parameter search</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/grid_search.html#alternatives-to-brute-force-parameter-search">3.2.5. Alternatives to brute force parameter search</a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="modules/model_evaluation.html">3.3. Metrics and scoring: quantifying the quality of predictions</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/model_evaluation.html#the-scoring-parameter-defining-model-evaluation-rules">3.3.1. The <code class="docutils literal notranslate"><span class="pre">scoring</span></code> parameter: defining model evaluation rules</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/model_evaluation.html#classification-metrics">3.3.2. Classification metrics</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/model_evaluation.html#multilabel-ranking-metrics">3.3.3. Multilabel ranking metrics</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/model_evaluation.html#regression-metrics">3.3.4. Regression metrics</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/model_evaluation.html#clustering-metrics">3.3.5. Clustering metrics</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/model_evaluation.html#dummy-estimators">3.3.6. Dummy estimators</a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="modules/learning_curve.html">3.4. Validation curves: plotting scores to evaluate models</a><ul>
<li class="toctree-l2"><a class="reference internal" href="modules/learning_curve.html#validation-curve">3.4.1. Validation curve</a></li>
<li class="toctree-l2"><a class="reference internal" href="modules/learning_curve.html#learning-curve">3.4.2. Learning curve</a></li>
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